Palm vein recognition research is progressing rapidly, underscoring the growing urgency for security studies in this field. A pivotal component of this research, anti-spoofing is currently hindered primarily by the scarcity of spoofing datasets. The scarcity stems from the immature methods employed in generating more realistic spoofing samples in contactless environments. This paper proposed an innovative method that enables generating convincingly realistic spoofing samples feasible. We observe considerable luminance distribution discrepancies between the actual palm and its captured image which are caused by non-uniform illumination. Through modeling and theoretical analysis, the issue of non-uniform illumination in contactless palm vein imaging is effectively addressed and remedied, subsequently enables the generation of more realistic spoofing samples. The experimental results demonstrate that the fabricated spoofing samples exhibit notable realism, with deception rates spanning from 87.37% to 88.83%, thereby verifying the viability of the proposed method.